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Related Experiment Video

Updated: Dec 31, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

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Robust face alignment by cascaded regression and de-occlusion.

Jun Wan1, Jing Li2, Zhihui Lai3

  • 1College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China; School of Computer Science, Wuhan University, Wuhan, China.

Neural Networks : the Official Journal of the International Neural Network Society
|December 31, 2019
PubMed
Summary

This study introduces a novel cascaded deep generative regression model for robust face alignment, effectively handling occlusions by de-occluding faces and enhancing landmark localization accuracy.

Keywords:
Deep regressionFace de-occlusionGenerative adversarial networkHeatmapPartial occlusion

Related Experiment Videos

Last Updated: Dec 31, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Area of Science:

  • Computer Vision
  • Facial Behavior Analysis

Background:

  • Face alignment performance degrades significantly with partial occlusions.
  • Accurate facial landmark localization is crucial for many computer vision tasks.

Purpose of the Study:

  • To develop a robust and occlusion-free face alignment algorithm.
  • To improve the mapping between facial appearance features and shape increments.

Main Methods:

  • Integrated a face de-occlusion module (disentangled representation learning Generative Adversarial Networks - GANs) and a deep regression module.
  • Employed a cascaded deep generative regression model for gradual face recovery and landmark localization.

Main Results:

  • The proposed model effectively locates occlusions and recovers genuine facial appearances.
  • Enhanced facial appearance representations led to more accurate regressors.
  • Outperformed state-of-the-art methods on four challenging occluded face datasets.

Conclusions:

  • The cascaded deep generative regression model offers a robust solution for face alignment under occlusion.
  • Recovered genuine faces significantly improve the accuracy of landmark localization.